Overview

Dataset statistics

Number of variables42
Number of observations20.000
Missing cells32.430
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 MiB
Average record size in memory336.0 B

Variable types

NUM17
CAT13
BOOL12

Warnings

grau_instrucao has constant value "20000" Constant
possui_telefone_celular has constant value "20000" Constant
codigo_area_telefone_residencial has a high cardinality: 81 distinct values High cardinality
codigo_area_telefone_trabalho has a high cardinality: 77 distinct values High cardinality
qtde_contas_bancarias_especiais is highly correlated with qtde_contas_bancariasHigh correlation
qtde_contas_bancarias is highly correlated with qtde_contas_bancarias_especiaisHigh correlation
local_onde_trabalha is highly correlated with local_onde_resideHigh correlation
local_onde_reside is highly correlated with local_onde_trabalhaHigh correlation
qtde_contas_bancarias_especiais is highly correlated with qtde_contas_bancariasHigh correlation
qtde_contas_bancarias is highly correlated with qtde_contas_bancarias_especiaisHigh correlation
tipo_residencia has 536 (2.7%) missing values Missing
meses_na_residencia has 1450 (7.3%) missing values Missing
profissao has 3095 (15.5%) missing values Missing
ocupacao has 2978 (14.9%) missing values Missing
profissao_companheiro has 11511 (57.6%) missing values Missing
grau_instrucao_companheiro has 12860 (64.3%) missing values Missing
renda_mensal_regular is highly skewed (γ1 = 67.75421325) Skewed
renda_extra is highly skewed (γ1 = 137.4095781) Skewed
valor_patrimonio_pessoal is highly skewed (γ1 = 126.6995194) Skewed
meses_no_trabalho is highly skewed (γ1 = 63.19895877) Skewed
id_solicitante has unique values Unique
qtde_dependentes has 13350 (66.8%) zeros Zeros
tipo_residencia has 331 (1.7%) zeros Zeros
meses_na_residencia has 1858 (9.3%) zeros Zeros
renda_extra has 18930 (94.7%) zeros Zeros
valor_patrimonio_pessoal has 19072 (95.4%) zeros Zeros
meses_no_trabalho has 19973 (99.9%) zeros Zeros
profissao has 1398 (7.0%) zeros Zeros
ocupacao has 1114 (5.6%) zeros Zeros
profissao_companheiro has 5551 (27.8%) zeros Zeros
grau_instrucao_companheiro has 6485 (32.4%) zeros Zeros

Reproduction

Analysis started2021-05-24 18:17:51.613677
Analysis finished2021-05-24 18:18:51.566884
Duration59.95 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

id_solicitante
Real number (ℝ≥0)

UNIQUE

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10000.5
Minimum1
Maximum20000
Zeros0
Zeros (%)0.0%
Memory size156.2 KiB
2021-05-24T15:18:51.776779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1000.95
Q15000.75
median10000.5
Q315000.25
95-th percentile19000.05
Maximum20000
Range19999
Interquartile range (IQR)9999.5

Descriptive statistics

Standard deviation5773.647028
Coefficient of variation (CV)0.577335836
Kurtosis-1.2
Mean10000.5
Median Absolute Deviation (MAD)5000
Skewness0
Sum200010000
Variance33335000
MonotocityStrictly increasing
2021-05-24T15:18:51.979278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
109121< 0.1%
 
129471< 0.1%
 
27081< 0.1%
 
6611< 0.1%
 
68061< 0.1%
 
47591< 0.1%
 
191001< 0.1%
 
170531< 0.1%
 
88651< 0.1%
 
Other values (19990)19990> 99.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
200001< 0.1%
 
199991< 0.1%
 
199981< 0.1%
 
199971< 0.1%
 
199961< 0.1%
 
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
17023 
2
2435 
7
 
542
ValueCountFrequency (%) 
11702385.1%
 
2243512.2%
 
75422.7%
 
2021-05-24T15:18:52.161617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:52.260279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:52.362401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

dia_vencimento
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.14725
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Memory size156.2 KiB
2021-05-24T15:18:52.471859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median10
Q320
95-th percentile25
Maximum25
Range24
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.748506839
Coefficient of variation (CV)0.5133017809
Kurtosis-0.7233846608
Mean13.14725
Median Absolute Deviation (MAD)5
Skewness0.441538168
Sum262945
Variance45.54234455
MonotocityNot monotonic
2021-05-24T15:18:52.592978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
10784739.2%
 
15355717.8%
 
25308915.4%
 
5282514.1%
 
2019529.8%
 
17303.6%
 
ValueCountFrequency (%) 
17303.6%
 
5282514.1%
 
10784739.2%
 
15355717.8%
 
2019529.8%
 
ValueCountFrequency (%) 
25308915.4%
 
2019529.8%
 
15355717.8%
 
10784739.2%
 
5282514.1%
 
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
internet
11264 
presencial
7855 
correio
 
881
ValueCountFrequency (%) 
internet1126456.3%
 
presencial785539.3%
 
correio8814.4%
 
2021-05-24T15:18:52.729914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:52.820251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:52.926929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length8
Mean length8.74145
Min length7

tipo_endereco
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
19873 
2
 
127
ValueCountFrequency (%) 
11987399.4%
 
21270.6%
 
2021-05-24T15:18:53.176663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:53.271850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:53.364650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

sexo
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
F
12246 
M
7722 
N
 
25
 
7
ValueCountFrequency (%) 
F1224661.2%
 
M772238.6%
 
N250.1%
 
7< 0.1%
 
2021-05-24T15:18:53.501280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:53.598981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:53.709367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

idade
Real number (ℝ≥0)

Distinct84
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.3525
Minimum7
Maximum106
Zeros0
Zeros (%)0.0%
Memory size156.2 KiB
2021-05-24T15:18:53.852770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile21
Q131
median40
Q352
95-th percentile70
Maximum106
Range99
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.93017713
Coefficient of variation (CV)0.3525217433
Kurtosis-0.210705359
Mean42.3525
Median Absolute Deviation (MAD)10
Skewness0.5584304521
Sum847050
Variance222.9101893
MonotocityNot monotonic
2021-05-24T15:18:54.017309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
405552.8%
 
395342.7%
 
365262.6%
 
325182.6%
 
375132.6%
 
435102.5%
 
285092.5%
 
335042.5%
 
315032.5%
 
385002.5%
 
Other values (74)1482874.1%
 
ValueCountFrequency (%) 
71< 0.1%
 
177< 0.1%
 
182651.3%
 
192601.3%
 
202931.5%
 
ValueCountFrequency (%) 
1062< 0.1%
 
1001< 0.1%
 
971< 0.1%
 
962< 0.1%
 
954< 0.1%
 

estado_civil
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.12085
Minimum0
Maximum7
Zeros81
Zeros (%)0.4%
Memory size156.2 KiB
2021-05-24T15:18:54.157331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.33200375
Coefficient of variation (CV)0.6280518423
Kurtosis2.799170933
Mean2.12085
Median Absolute Deviation (MAD)0
Skewness1.76004596
Sum42417
Variance1.774233989
MonotocityNot monotonic
2021-05-24T15:18:54.270310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
21008850.4%
 
1651932.6%
 
415737.9%
 
67633.8%
 
55222.6%
 
32341.2%
 
72201.1%
 
0810.4%
 
ValueCountFrequency (%) 
0810.4%
 
1651932.6%
 
21008850.4%
 
32341.2%
 
415737.9%
 
ValueCountFrequency (%) 
72201.1%
 
67633.8%
 
55222.6%
 
415737.9%
 
32341.2%
 

qtde_dependentes
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6664
Minimum0
Maximum53
Zeros13350
Zeros (%)66.8%
Memory size156.2 KiB
2021-05-24T15:18:54.393724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum53
Range53
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.23672451
Coefficient of variation (CV)1.855829097
Kurtosis167.6045062
Mean0.6664
Median Absolute Deviation (MAD)0
Skewness5.925042325
Sum13328
Variance1.529487514
MonotocityNot monotonic
2021-05-24T15:18:54.525661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
01335066.8%
 
1281414.1%
 
2218910.9%
 
310295.1%
 
43521.8%
 
51490.7%
 
6570.3%
 
7220.1%
 
8140.1%
 
99< 0.1%
 
Other values (5)150.1%
 
ValueCountFrequency (%) 
01335066.8%
 
1281414.1%
 
2218910.9%
 
310295.1%
 
43521.8%
 
ValueCountFrequency (%) 
531< 0.1%
 
141< 0.1%
 
132< 0.1%
 
114< 0.1%
 
107< 0.1%
 

grau_instrucao
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
20000 
ValueCountFrequency (%) 
020000100.0%
 
2021-05-24T15:18:54.622138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

nacionalidade
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
19152 
0
 
808
2
 
40
ValueCountFrequency (%) 
11915295.8%
 
08084.0%
 
2400.2%
 
2021-05-24T15:18:54.717127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:54.819918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:54.921146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1
Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
BA
2351 
SP
2336 
RS
1919 
CE
1910 
PE
1651 
Other values (23)
9833 
ValueCountFrequency (%) 
BA235111.8%
 
SP233611.7%
 
RS19199.6%
 
CE19109.6%
 
PE16518.3%
 
MG14467.2%
 
RN8274.1%
 
8224.1%
 
PR7643.8%
 
RJ7203.6%
 
Other values (18)525426.3%
 
2021-05-24T15:18:55.068496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:55.215026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.9589
Min length1
Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
SP
3578 
BA
2045 
RS
1995 
CE
1865 
PE
1484 
Other values (22)
9033 
ValueCountFrequency (%) 
SP357817.9%
 
BA204510.2%
 
RS199510.0%
 
CE18659.3%
 
PE14847.4%
 
MG11875.9%
 
PA9274.6%
 
RJ8634.3%
 
RN8464.2%
 
GO6823.4%
 
Other values (17)452822.6%
 
2021-05-24T15:18:55.371930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:55.521928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
16474 
N
3526 
ValueCountFrequency (%) 
Y1647482.4%
 
N352617.6%
 
2021-05-24T15:18:55.610224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

codigo_area_telefone_residencial
Categorical

HIGH CARDINALITY

Distinct81
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3534 
5
1838 
107
 
1142
97
 
1142
54
 
904
Other values (76)
11440 
ValueCountFrequency (%) 
353417.7%
 
518389.2%
 
10711425.7%
 
9711425.7%
 
549044.5%
 
1056463.2%
 
845452.7%
 
815352.7%
 
205342.7%
 
585182.6%
 
Other values (71)866243.3%
 
2021-05-24T15:18:55.740140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique11 ?
Unique (%)0.1%
2021-05-24T15:18:55.909642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length1.9452
Min length1

tipo_residencia
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing536
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean1.261302918
Minimum0
Maximum5
Zeros331
Zeros (%)1.7%
Memory size156.2 KiB
2021-05-24T15:18:56.029094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8835795418
Coefficient of variation (CV)0.7005292139
Kurtosis11.37714604
Mean1.261302918
Median Absolute Deviation (MAD)0
Skewness3.408604224
Sum24550
Variance0.7807128068
MonotocityNot monotonic
2021-05-24T15:18:56.271872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
11649782.5%
 
216358.2%
 
58274.1%
 
03311.7%
 
41260.6%
 
3480.2%
 
(Missing)5362.7%
 
ValueCountFrequency (%) 
03311.7%
 
11649782.5%
 
216358.2%
 
3480.2%
 
41260.6%
 
ValueCountFrequency (%) 
58274.1%
 
41260.6%
 
3480.2%
 
216358.2%
 
11649782.5%
 

meses_na_residencia
Real number (ℝ≥0)

MISSING
ZEROS

Distinct76
Distinct (%)0.4%
Missing1450
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean9.57245283
Minimum0
Maximum228
Zeros1858
Zeros (%)9.3%
Memory size156.2 KiB
2021-05-24T15:18:56.416383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q315
95-th percentile30
Maximum228
Range228
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.64958027
Coefficient of variation (CV)1.112523661
Kurtosis18.11111445
Mean9.57245283
Median Absolute Deviation (MAD)5
Skewness2.340849526
Sum177569
Variance113.4135599
MonotocityNot monotonic
2021-05-24T15:18:56.586019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1293714.7%
 
018589.3%
 
1015107.5%
 
514867.4%
 
213196.6%
 
39534.8%
 
209344.7%
 
157763.9%
 
86723.4%
 
66663.3%
 
Other values (66)543927.2%
 
(Missing)14507.2%
 
ValueCountFrequency (%) 
018589.3%
 
1293714.7%
 
213196.6%
 
39534.8%
 
46433.2%
 
ValueCountFrequency (%) 
2281< 0.1%
 
2001< 0.1%
 
1001< 0.1%
 
961< 0.1%
 
891< 0.1%
 

possui_telefone_celular
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
20000 
ValueCountFrequency (%) 
N20000100.0%
 
2021-05-24T15:18:56.740127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:56.826159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:56.909538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
15984 
0
4016 
ValueCountFrequency (%) 
11598479.9%
 
0401620.1%
 
2021-05-24T15:18:56.998504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

renda_mensal_regular
Real number (ℝ≥0)

SKEWED

Distinct3031
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean957.1309375
Minimum69
Maximum959000
Zeros0
Zeros (%)0.0%
Memory size156.2 KiB
2021-05-24T15:18:57.109338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum69
5-th percentile289
Q1360
median500
Q3800
95-th percentile1782.05
Maximum959000
Range958931
Interquartile range (IQR)440

Descriptive statistics

Standard deviation11353.965
Coefficient of variation (CV)11.86249922
Kurtosis5062.489381
Mean957.1309375
Median Absolute Deviation (MAD)150
Skewness67.75421325
Sum19142618.75
Variance128912521.2
MonotocityNot monotonic
2021-05-24T15:18:57.292109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
350280814.0%
 
5006283.1%
 
4005792.9%
 
3805462.7%
 
6005132.6%
 
7004192.1%
 
8003881.9%
 
4503401.7%
 
3003371.7%
 
10002481.2%
 
Other values (3021)1319466.0%
 
ValueCountFrequency (%) 
691< 0.1%
 
1005< 0.1%
 
1051< 0.1%
 
1151< 0.1%
 
1205< 0.1%
 
ValueCountFrequency (%) 
9590001< 0.1%
 
8750001< 0.1%
 
6680001< 0.1%
 
4867781< 0.1%
 
1742741< 0.1%
 

renda_extra
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct284
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.0969585
Minimum0
Maximum194344
Zeros18930
Zeros (%)94.7%
Memory size156.2 KiB
2021-05-24T15:18:57.474274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile100
Maximum194344
Range194344
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1387.42878
Coefficient of variation (CV)35.48687247
Kurtosis19237.66506
Mean39.0969585
Median Absolute Deviation (MAD)0
Skewness137.4095781
Sum781939.17
Variance1924958.62
MonotocityNot monotonic
2021-05-24T15:18:57.651474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01893094.7%
 
3501360.7%
 
600610.3%
 
300580.3%
 
400570.3%
 
200570.3%
 
500530.3%
 
800310.2%
 
250290.1%
 
150250.1%
 
Other values (274)5632.8%
 
ValueCountFrequency (%) 
01893094.7%
 
11< 0.1%
 
31< 0.1%
 
152< 0.1%
 
31.481< 0.1%
 
ValueCountFrequency (%) 
1943441< 0.1%
 
102001< 0.1%
 
83411< 0.1%
 
54001< 0.1%
 
50001< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
17822 
1
2178 
ValueCountFrequency (%) 
01782289.1%
 
1217810.9%
 
2021-05-24T15:18:57.784704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
18101 
1
1899 
ValueCountFrequency (%) 
01810190.5%
 
118999.5%
 
2021-05-24T15:18:57.836821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
19968 
1
 
32
ValueCountFrequency (%) 
01996899.8%
 
1320.2%
 
2021-05-24T15:18:57.888143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
19959 
1
 
41
ValueCountFrequency (%) 
01995999.8%
 
1410.2%
 
2021-05-24T15:18:57.940395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
19955 
1
 
45
ValueCountFrequency (%) 
01995599.8%
 
1450.2%
 
2021-05-24T15:18:57.991680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

qtde_contas_bancarias
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
12786 
1
7206 
2
 
8
ValueCountFrequency (%) 
01278663.9%
 
1720636.0%
 
28< 0.1%
 
2021-05-24T15:18:58.084952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:58.174233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:58.276332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

qtde_contas_bancarias_especiais
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
12786 
1
7206 
2
 
8
ValueCountFrequency (%) 
01278663.9%
 
1720636.0%
 
28< 0.1%
 
2021-05-24T15:18:58.405826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:58.495001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:58.598176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

valor_patrimonio_pessoal
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct94
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2095.614
Minimum0
Maximum6000000
Zeros19072
Zeros (%)95.4%
Memory size156.2 KiB
2021-05-24T15:18:58.741726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6000000
Range6000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation44033.43658
Coefficient of variation (CV)21.01218859
Kurtosis17218.03756
Mean2095.614
Median Absolute Deviation (MAD)0
Skewness126.6995194
Sum41912280
Variance1938943537
MonotocityNot monotonic
2021-05-24T15:18:59.030478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01907295.4%
 
25000870.4%
 
30000860.4%
 
20000830.4%
 
50000710.4%
 
15000660.3%
 
35000630.3%
 
40000480.2%
 
45000390.2%
 
60000370.2%
 
Other values (84)3481.7%
 
ValueCountFrequency (%) 
01907295.4%
 
71< 0.1%
 
151< 0.1%
 
171< 0.1%
 
182< 0.1%
 
ValueCountFrequency (%) 
60000001< 0.1%
 
6000001< 0.1%
 
4500001< 0.1%
 
3200001< 0.1%
 
2500002< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
13219 
1
6781 
ValueCountFrequency (%) 
01321966.1%
 
1678133.9%
 
2021-05-24T15:18:59.155773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
11174 
Y
8826 
ValueCountFrequency (%) 
N1117455.9%
 
Y882644.1%
 
2021-05-24T15:18:59.207584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
13573 
SP
 
1010
RS
 
819
CE
 
588
BA
 
569
Other values (23)
3441 
ValueCountFrequency (%) 
1357367.9%
 
SP10105.1%
 
RS8194.1%
 
CE5882.9%
 
BA5692.8%
 
MG5002.5%
 
PE3691.8%
 
PA3161.6%
 
PR2361.2%
 
RJ2291.1%
 
Other values (18)17919.0%
 
2021-05-24T15:18:59.319581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-24T15:18:59.469807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.32135
Min length1
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
14519 
Y
5481 
ValueCountFrequency (%) 
N1451972.6%
 
Y548127.4%
 
2021-05-24T15:18:59.563269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

codigo_area_telefone_trabalho
Categorical

HIGH CARDINALITY

Distinct77
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
14525 
5
 
631
54
 
442
107
 
407
97
 
264
Other values (72)
3731 
ValueCountFrequency (%) 
1452572.6%
 
56313.2%
 
544422.2%
 
1074072.0%
 
972641.3%
 
811961.0%
 
291870.9%
 
661840.9%
 
1051820.9%
 
581780.9%
 
Other values (67)280414.0%
 
2021-05-24T15:18:59.695053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)< 0.1%
2021-05-24T15:18:59.871108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.30375
Min length1

meses_no_trabalho
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0089
Minimum0
Maximum32
Zeros19973
Zeros (%)99.9%
Memory size156.2 KiB
2021-05-24T15:19:00.001111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum32
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3888808962
Coefficient of variation (CV)43.69448272
Kurtosis4536.037419
Mean0.0089
Median Absolute Deviation (MAD)0
Skewness63.19895877
Sum178
Variance0.1512283514
MonotocityNot monotonic
2021-05-24T15:19:00.121796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
01997399.9%
 
17< 0.1%
 
34< 0.1%
 
24< 0.1%
 
62< 0.1%
 
52< 0.1%
 
42< 0.1%
 
151< 0.1%
 
301< 0.1%
 
141< 0.1%
 
Other values (3)3< 0.1%
 
ValueCountFrequency (%) 
01997399.9%
 
17< 0.1%
 
24< 0.1%
 
34< 0.1%
 
42< 0.1%
 
ValueCountFrequency (%) 
321< 0.1%
 
301< 0.1%
 
181< 0.1%
 
151< 0.1%
 
141< 0.1%
 

profissao
Real number (ℝ≥0)

MISSING
ZEROS

Distinct19
Distinct (%)0.1%
Missing3095
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean8.045607808
Minimum0
Maximum18
Zeros1398
Zeros (%)7.0%
Memory size156.2 KiB
2021-05-24T15:19:00.254938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median9
Q39
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.211523085
Coefficient of variation (CV)0.3991647569
Kurtosis1.647157368
Mean8.045607808
Median Absolute Deviation (MAD)0
Skewness-1.482972287
Sum136011
Variance10.31388052
MonotocityNot monotonic
2021-05-24T15:19:00.383270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
91210360.5%
 
013987.0%
 
1113496.7%
 
211715.9%
 
121921.0%
 
101730.9%
 
161260.6%
 
131250.6%
 
7900.4%
 
8610.3%
 
Other values (9)1170.6%
 
(Missing)309515.5%
 
ValueCountFrequency (%) 
013987.0%
 
11< 0.1%
 
211715.9%
 
37< 0.1%
 
4130.1%
 
ValueCountFrequency (%) 
181< 0.1%
 
17160.1%
 
161260.6%
 
15250.1%
 
146< 0.1%
 

ocupacao
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing2978
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean2.533309834
Minimum0
Maximum5
Zeros1114
Zeros (%)5.6%
Memory size156.2 KiB
2021-05-24T15:19:00.509258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.532765217
Coefficient of variation (CV)0.6050445137
Kurtosis-1.064418569
Mean2.533309834
Median Absolute Deviation (MAD)1
Skewness0.3443705658
Sum43122
Variance2.34936921
MonotocityNot monotonic
2021-05-24T15:19:00.627325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
2688234.4%
 
1314415.7%
 
4292414.6%
 
5282214.1%
 
011145.6%
 
31360.7%
 
(Missing)297814.9%
 
ValueCountFrequency (%) 
011145.6%
 
1314415.7%
 
2688234.4%
 
31360.7%
 
4292414.6%
 
ValueCountFrequency (%) 
5282214.1%
 
4292414.6%
 
31360.7%
 
2688234.4%
 
1314415.7%
 

profissao_companheiro
Real number (ℝ≥0)

MISSING
ZEROS

Distinct17
Distinct (%)0.2%
Missing11511
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean3.711509012
Minimum0
Maximum18
Zeros5551
Zeros (%)27.8%
Memory size156.2 KiB
2021-05-24T15:19:00.749631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.184218121
Coefficient of variation (CV)1.39679524
Kurtosis-1.352740291
Mean3.711509012
Median Absolute Deviation (MAD)0
Skewness0.7297457824
Sum31507
Variance26.87611753
MonotocityNot monotonic
2021-05-24T15:19:00.872854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
0555127.8%
 
11235811.8%
 
94092.0%
 
16780.4%
 
2390.2%
 
12150.1%
 
10130.1%
 
69< 0.1%
 
135< 0.1%
 
173< 0.1%
 
Other values (7)9< 0.1%
 
(Missing)1151157.6%
 
ValueCountFrequency (%) 
0555127.8%
 
11< 0.1%
 
2390.2%
 
31< 0.1%
 
69< 0.1%
 
ValueCountFrequency (%) 
181< 0.1%
 
173< 0.1%
 
16780.4%
 
151< 0.1%
 
141< 0.1%
 

grau_instrucao_companheiro
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)0.1%
Missing12860
Missing (%)64.3%
Infinite0
Infinite (%)0.0%
Mean0.2880952381
Minimum0
Maximum5
Zeros6485
Zeros (%)32.4%
Memory size156.2 KiB
2021-05-24T15:19:01.002152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9443388652
Coefficient of variation (CV)3.277870441
Kurtosis8.751956221
Mean0.2880952381
Median Absolute Deviation (MAD)0
Skewness3.183826767
Sum2057
Variance0.8917758923
MonotocityNot monotonic
2021-05-24T15:19:01.122697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
0648532.4%
 
32451.2%
 
42441.2%
 
21320.7%
 
1220.1%
 
5120.1%
 
(Missing)1286064.3%
 
ValueCountFrequency (%) 
0648532.4%
 
1220.1%
 
21320.7%
 
32451.2%
 
42441.2%
 
ValueCountFrequency (%) 
5120.1%
 
42441.2%
 
32451.2%
 
21320.7%
 
1220.1%
 

local_onde_reside
Real number (ℝ≥0)

HIGH CORRELATION

Distinct743
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean581.29525
Minimum105
Maximum999
Zeros0
Zeros (%)0.0%
Memory size156.2 KiB
2021-05-24T15:19:01.268409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile148
Q1444
median596
Q3728
95-th percentile956
Maximum999
Range894
Interquartile range (IQR)284

Descriptive statistics

Standard deviation227.369798
Coefficient of variation (CV)0.3911433957
Kurtosis-0.5758248011
Mean581.29525
Median Absolute Deviation (MAD)144
Skewness-0.2500355883
Sum11625905
Variance51697.02503
MonotocityNot monotonic
2021-05-24T15:19:01.430378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9603671.8%
 
5913451.7%
 
5703101.6%
 
4562561.3%
 
6282491.2%
 
6852221.1%
 
5962051.0%
 
6891961.0%
 
6191941.0%
 
5811890.9%
 
Other values (733)1746787.3%
 
ValueCountFrequency (%) 
1051< 0.1%
 
110110.1%
 
1121< 0.1%
 
113830.4%
 
114460.2%
 
ValueCountFrequency (%) 
9992< 0.1%
 
9981< 0.1%
 
9974< 0.1%
 
9962< 0.1%
 
9958< 0.1%
 

local_onde_trabalha
Real number (ℝ≥0)

HIGH CORRELATION

Distinct743
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean581.29525
Minimum105
Maximum999
Zeros0
Zeros (%)0.0%
Memory size156.2 KiB
2021-05-24T15:19:01.600272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile148
Q1444
median596
Q3728
95-th percentile956
Maximum999
Range894
Interquartile range (IQR)284

Descriptive statistics

Standard deviation227.369798
Coefficient of variation (CV)0.3911433957
Kurtosis-0.5758248011
Mean581.29525
Median Absolute Deviation (MAD)144
Skewness-0.2500355883
Sum11625905
Variance51697.02503
MonotocityNot monotonic
2021-05-24T15:19:01.893833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9603671.8%
 
5913451.7%
 
5703101.6%
 
4562561.3%
 
6282491.2%
 
6852221.1%
 
5962051.0%
 
6891961.0%
 
6191941.0%
 
5811890.9%
 
Other values (733)1746787.3%
 
ValueCountFrequency (%) 
1051< 0.1%
 
110110.1%
 
1121< 0.1%
 
113830.4%
 
114460.2%
 
ValueCountFrequency (%) 
9992< 0.1%
 
9981< 0.1%
 
9974< 0.1%
 
9962< 0.1%
 
9958< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 
0
10000 
ValueCountFrequency (%) 
11000050.0%
 
01000050.0%
 
2021-05-24T15:19:02.006026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Interactions

2021-05-24T15:18:04.625942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:05.101477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:05.257402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:05.416283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:05.580205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:05.743452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:05.900228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:06.057053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:06.211289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:06.367579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:06.522069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:06.788635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:06.952505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:07.106019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:07.270366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:07.426132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:07.577789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-24T15:18:37.234221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:37.373076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:37.508995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:37.644018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:37.811191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:38.074479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:38.220254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:38.376130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:38.532073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:38.680799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:38.833340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:38.980909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:39.131010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:39.280154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:39.434098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:39.590012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:39.738333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:39.897260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:40.048023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:40.194520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:40.342209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:40.492952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:40.632490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:40.768186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:40.914795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:41.061170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:41.200525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:41.340508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:41.477719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:41.617047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:41.755719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:41.899079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:42.044784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:42.182330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:42.332180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:42.586968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:42.723964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:42.861772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:43.014460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:43.150065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:43.280411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:43.419945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:43.560705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:43.693809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:43.829324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:43.961437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:44.094995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:44.228750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:44.367275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:44.508088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:44.640591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:44.785225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:44.919716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:45.050796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:45.181824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:45.330054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:45.464186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:45.593945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:45.734070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:45.875736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:46.011369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:46.145582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:46.278855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:46.413242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:46.547574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:46.685329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:46.944535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:47.076680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:47.220680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:47.357291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:47.488885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-24T15:19:02.142695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-24T15:19:02.614426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-24T15:19:03.089084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-24T15:19:03.584190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-24T15:19:04.089345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-24T15:18:47.978911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:50.150761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:50.929312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-24T15:18:51.216007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

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01110presencial1M852001CECEY1071.012.0N0480.00.0000001101NN09.01.00.00.06006000
12125internet1F381001SESEY911.05.0N1380.00.0000000000NN02.05.0NaNNaN4924920
23120internet1F372001BABAY905.01.0N1600.00.0000000000NN07.0NaN15.0NaN4504501
34120internet1M371101RSRSY541.01.0N1460.00.0000000000YRSY5409.02.018.0NaN9329321
4571internet1F511301BABAY860.01.0N1687.0600.0000000001YBAN09.05.0NaNNaN4404401
56120presencial1M211101CECEY1075.02.0N0382.00.0100000001YCEY10709.02.00.00.06286281
67115presencial1F644201SPSPY161.00.0N1350.00.0000001101NN010.01.00.00.01901901
7815internet1F201001ESESY251.05.0N1800.00.0000000000NN018.0NaN7.0NaN2992991
89225internet1F392201GOGOY671.03.0N11200.00.0100000000YY6909.02.09.04.07567560
910110presencial1M442201RSRSN1.015.0N0749.00.0000001101YRSN09.02.016.04.09609601

Last rows

id_solicitanteproduto_solicitadodia_vencimentoforma_envio_solicitacaotipo_enderecosexoidadeestado_civilqtde_dependentesgrau_instrucaonacionalidadeestado_onde_nasceuestado_onde_residepossui_telefone_residencialcodigo_area_telefone_residencialtipo_residenciameses_na_residenciapossui_telefone_celularpossui_emailrenda_mensal_regularrenda_extrapossui_cartao_visapossui_cartao_mastercardpossui_cartao_dinerspossui_cartao_amexpossui_outros_cartoesqtde_contas_bancariasqtde_contas_bancarias_especiaisvalor_patrimonio_pessoalpossui_carrovinculo_formal_com_empresaestado_onde_trabalhapossui_telefone_trabalhocodigo_area_telefone_trabalhomeses_no_trabalhoprofissaoocupacaoprofissao_companheirograu_instrucao_companheirolocal_onde_residelocal_onde_trabalhainadimplente
1999019991110presencial1F524001SPPRN1.00.0N0350.00.0000001101NN00.01.00.00.08728721
1999119992110presencial1M482201MGMGN1.06.0N01308.00.0000001101NN00.01.00.00.03513511
199921999315internet1M624001ESRJY201.030.0N1358.00.0000000000NN09.01.0NaNNaN2302300
199931999415internet1F181001RJRJY221.06.0N1405.00.0000000000NN09.02.0NaNNaN2892890
199941999525presencial1M232001BABAY841.023.0N0350.00.0000001101NN00.00.00.00.04574571
1999519996110presencial1M272001MGMGY292.00.0N1423.00.0000001101YN09.01.00.00.03083080
1999619997120presencial1F262101CECEY1071.03.0N0350.00.0000001101YN09.02.00.00.06396390
1999719998110internet1F632001BABAY865.025.0N1321.00.0000000000NN09.01.0NaNNaN4864860
199981999915internet1F841001PBRNN1.030.0N1380.00.0000000000NN0NaNNaNNaNNaN5905900
1999920000220presencial1F531001MASPY51.011.0N1300.00.0000001101NN09.05.00.00.01321320